Normalized Scalar Product Approach for Nearshore Bathymetric Estimation From X-Band Radar Images: An Assessment Based on Simulated and Measured Data

This paper investigates the capability of X-band radar systems to estimate nearshore bathymetry fields by considering both simulated and measured radar data. For the first time, a sensitivity analysis is performed to evaluate how sea-state conditions affect bathymetric estimates. For this purpose, sea wave fields generated by means of a numerical model, based on a nonlinear shallow-water equation solver, are used. Starting from the synthetic radar data, which represent the input of the bathymetric estimation algorithm, the bathymetric reconstruction is performed through the normalized scalar product (NSP) estimation strategy, exploiting a spatial partitioning of the radar data. In this way, it is possible to improve the accuracy of the estimates in nearshore areas, where the space-varying behavior of the sea depth and the presence of coastlines or coastal structures typically leads to a spatial inhomogeneity of the wave motion. In this regard, it is shown how the choice of the partitioning settings affects the bathymetric estimates obtained from high-resolution X-band radar images by using the NSP strategy. In addition, an adaptive partitioning strategy that takes into account the wave evolution in nearshore shallow waters is devised. Based on both simulated and measured radar data, the accuracy of the bathymetric estimates achievable through the proposed adaptive partitioning process and that obtained by exploiting the approach using uniform spatial partitioning are compared. The results obtained confirm the robustness of the NSP technique with respect to sea conditions and, moreover, demonstrate that the proposed adaptive partitioning strategy provides more accurate bathymetric estimates than those obtained with the space-invariant partitioning procedure.